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  1. Article ; Online: A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding.

    Akbar, Rahmad / Robert, Philippe A / Pavlović, Milena / Jeliazkov, Jeliazko R / Snapkov, Igor / Slabodkin, Andrei / Weber, Cédric R / Scheffer, Lonneke / Miho, Enkelejda / Haff, Ingrid Hobæk / Haug, Dag Trygve Tryslew / Lund-Johansen, Fridtjof / Safonova, Yana / Sandve, Geir K / Greiff, Victor

    Cell reports

    2021  Volume 34, Issue 11, Page(s) 108856

    Abstract: Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the ... ...

    Abstract Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 10
    MeSH term(s) Amino Acid Motifs ; Amino Acid Sequence ; Antibodies/chemistry ; Antibodies/immunology ; Antigen-Antibody Reactions/immunology ; Binding Sites, Antibody/immunology ; Complementarity Determining Regions/chemistry ; Epitopes/chemistry ; Epitopes/immunology ; Machine Learning ; Protein Binding
    Chemical Substances Antibodies ; Complementarity Determining Regions ; Epitopes
    Language English
    Publishing date 2021-03-17
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2649101-1
    ISSN 2211-1247 ; 2211-1247
    ISSN (online) 2211-1247
    ISSN 2211-1247
    DOI 10.1016/j.celrep.2021.108856
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Individualized VDJ recombination predisposes the available Ig sequence space.

    Slabodkin, Andrei / Chernigovskaya, Maria / Mikocziova, Ivana / Akbar, Rahmad / Scheffer, Lonneke / Pavlović, Milena / Bashour, Habib / Snapkov, Igor / Mehta, Brij Bhushan / Weber, Cédric R / Gutierrez-Marcos, Jose / Sollid, Ludvig M / Haff, Ingrid Hobæk / Sandve, Geir Kjetil / Robert, Philippe A / Greiff, Victor

    Genome research

    2021  Volume 31, Issue 12, Page(s) 2209–2224

    Abstract: The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (auto)antigen recognition. VDJ recombination follows probabilistic rules ...

    Abstract The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin (Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (auto)antigen recognition. VDJ recombination follows probabilistic rules that can be modeled statistically. So far, it remains unknown whether VDJ recombination rules differ between individuals. If these rules differed, identical (auto)antigen-specific Ig sequences would be generated with individual-specific probabilities, signifying that the available Ig sequence space is individual specific. We devised a sensitivity-tested distance measure that enables inter-individual comparison of VDJ recombination models. We discovered, accounting for several sources of noise as well as allelic variation in Ig sequencing data, that not only unrelated individuals but also human monozygotic twins and even inbred mice possess statistically distinguishable immunoglobulin recombination models. This suggests that, in addition to genetic, there is also nongenetic modulation of VDJ recombination. We demonstrate that population-wide individualized VDJ recombination can result in orders of magnitude of difference in the probability to generate (auto)antigen-specific Ig sequences. Our findings have implications for immune receptor-based individualized medicine approaches relevant to vaccination, infection, and autoimmunity.
    Language English
    Publishing date 2021-11-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1284872-4
    ISSN 1549-5469 ; 1088-9051 ; 1054-9803
    ISSN (online) 1549-5469
    ISSN 1088-9051 ; 1054-9803
    DOI 10.1101/gr.275373.121
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction.

    Robert, Philippe A / Akbar, Rahmad / Frank, Robert / Pavlović, Milena / Widrich, Michael / Snapkov, Igor / Slabodkin, Andrei / Chernigovskaya, Maria / Scheffer, Lonneke / Smorodina, Eva / Rawat, Puneet / Mehta, Brij Bhushan / Vu, Mai Ha / Mathisen, Ingvild Frøberg / Prósz, Aurél / Abram, Krzysztof / Olar, Alex / Miho, Enkelejda / Haug, Dag Trygve Tryslew /
    Lund-Johansen, Fridtjof / Hochreiter, Sepp / Haff, Ingrid Hobæk / Klambauer, Günter / Sandve, Geir Kjetil / Greiff, Victor

    Nature computational science

    2022  Volume 2, Issue 12, Page(s) 845–865

    Abstract: Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of ...

    Abstract Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.
    MeSH term(s) Antibody Specificity ; Antibodies ; Epitopes/chemistry ; Antigen-Antibody Reactions ; Machine Learning
    Chemical Substances Antibodies ; Epitopes
    Language English
    Publishing date 2022-12-19
    Publishing country United States
    Document type Journal Article
    ISSN 2662-8457
    ISSN (online) 2662-8457
    DOI 10.1038/s43588-022-00372-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.

    Pavlović, Milena / Scheffer, Lonneke / Motwani, Keshav / Kanduri, Chakravarthi / Kompova, Radmila / Vazov, Nikolay / Waagan, Knut / Bernal, Fabian L M / Costa, Alexandre Almeida / Corrie, Brian / Akbar, Rahmad / Al Hajj, Ghadi S / Balaban, Gabriel / Brusko, Todd M / Chernigovskaya, Maria / Christley, Scott / Cowell, Lindsay G / Frank, Robert / Grytten, Ivar /
    Gundersen, Sveinung / Haff, Ingrid Hobæk / Hovig, Eivind / Hsieh, Ping-Han / Klambauer, Günter / Kuijjer, Marieke L / Lund-Andersen, Christin / Martini, Antonio / Minotto, Thomas / Pensar, Johan / Rand, Knut / Riccardi, Enrico / Robert, Philippe A / Rocha, Artur / Slabodkin, Andrei / Snapkov, Igor / Sollid, Ludvig M / Titov, Dmytro / Weber, Cédric R / Widrich, Michael / Yaari, Gur / Greiff, Victor / Sandve, Geir Kjetil

    Nature machine intelligence

    2021  Volume 3, Issue 11, Page(s) 936–944

    Abstract: Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal ... ...

    Abstract Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel deep learning method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.
    Language English
    Publishing date 2021-11-16
    Publishing country England
    Document type Journal Article
    ISSN 2522-5839
    ISSN (online) 2522-5839
    DOI 10.1038/s42256-021-00413-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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